Die Weltgesundheitsorganisation WHO schätzte die Zahl der an Malaria erkrankten Menschen im Jahr 2009 auf weltweit 225 Millionen. Auf dem afrikanischen Kontinent betrafen 85% der durch Malaria verursachten Todesfälle Kinder unter fünf Jahren. Obwohl die Inzidenzen der P. falciparum-Malaria in einigen Teilen des subsaharischen Afrika sinken und andere Erkrankungen mit ähnlichen Symptomen wie denen der Malaria an Bedeutung gewinnen, ist eine vorsorgliche medikamentöse Behandlung im Verdachtsfall weiterhin üblich. Ziel dieser Arbeit ist die Generierung eines auf das Lebensalter bezogenen klinischen Algorithmus, der mit einfachen klinischen Symptomen die Diagnose einer P. falciparum - Parasitämie ermöglicht. Die Studie wurde in einem ländlichen Krankenhaus in der Ashanti-Region in Ghana durchgeführt, welche über das ganze Jahr hinweg holoendemisch für Malaria ist. Insgesamt wurden 5447 ambulante Besuche von 3641 Patienten im Alter zwischen 2-60 Monaten analysiert. Alle Kinder wurden von einem Pädiater klinisch untersucht und es wurden ein kleines Blutbild sowie ein Malariaausstrich (‘Dicker Tropfen’) angefertigt. Mit Hilfe einesClassification and Regression Tree (CART) wurde ein klinischer Entscheidungsbaum für die Prädiktion einer Plasmodium-Parasitämie generiert und prädiktive Werte für alle erfassten Symptome berechnet. Eine Parasitämie wurde bei Kindern im Alter von 2-12 Monaten mit einer Prävalenz von 13.8% und bei Kindern im Alter zwischen 12 und 60 Monatenmit einer Prävalenz von 30.6% gefunden. Das CART-Modell ergab altersabhängige Unterschiede in der Fähigkeit der Variablen eine Parasitämie vorherzusagen. Während sich bei Kindern im Alter zwischen 2 und 12 Monaten die „palmare Blässe“ als das wichtigste Symptom herausstellte, gewannen die Variablen „Fieber in der Anamnese“ und „erhöhte Körpertemperatur ≥ 37.5°C“ bei Kindern im Alter zwischen 12 und 60 Monaten an Bedeutung. Die Variable „palmare Blässe“ war bei Kindern jedes Alters signifikant (p<0.001) mit niedrigeren Hämoglobinwerten assoziiert. Im Vergleich zum Algorithmus des Integrated Management of Childhood Illness (IMCI) hatte das CART-Modell eine deutlich höhere Spezifität sowie einen höheren positiven prädiktiven Wert für die Vorhersage einer Parasitämie. Die Anwendung von altersbezogenen Algorithmen erhöht die Spezifität der Vorhersage einer P. falciparum - Parasitämie. Selbst in einer Population mit einer hohen Prävalenz an Anämie ermöglicht der prädiktive Wert der „palmaren Blässe“ eine Erkennung von signifikant geringeren Hb-Werten. Die Bedeutung der „palmaren Blässe“ sollte daher in der Schulung von Gesundheitshelfern hervorgehoben werden. Mangels ausreichender Sensitivität kann allerdings weder auf Basis des besten Algorithmus noch mit „palmarer Blässe“ als einzelnem klinischem Zeichen eine Therapieentscheidung getroffen werden. Sie sind daher kein Ersatz für eine vorsorgliche medikamentöse Behandlung und einen Erregernachweis.

In the shift to a globalised creative economy where innovation and creativity are increasingly prized, many studies have documented direct and indirect social and economic benefits of the arts. In addition, arts workers have been argued to possess capabilities which are of great benefit both within and outside the arts, including (in addition to creativity) problem solving abilities, emotional intelligence, and team working skills (ARC Centre of Excellence for Creative Industries and Innovation, 2007). However, the labour force characteristics of professional artists in Australia and elsewhere belie their importance. The average earnings of workers in the arts sector are consistently less than other workers with similar educational backgrounds, and their rates of unemployment and underemployment are much higher (Australian Bureau of Statistics, 2005; Caves, 2000; Throsby & Hollister, 2003). Graduating students in the arts appear to experience similar employment challenges and exhibit similar patterns of work to artists in general. Many eventually obtain work unrelated to the arts or go back to university to complete further tertiary study in fields unrelated to arts (Graduate Careers Council of Australia, 2005a). Recent developments in career development theory have involved discussion of the rise of boundaryless careers amongst knowledge workers. Boundaryless careers are characterised by non-linear career progression occurring outside the bounds of a single organisation or field (Arthur & Rousseau, 1996a, 1996b). The protean career is an extreme form of the boundaryless career, where the careerist also possesses strong internal career motivations and criteria for success (Baruch, 2004; Hall, 2004; Hall & Mirvis, 1996). It involves a psychological contract with one's self rather than an organisation or organisations. The boundaryless and protean career literature suggests competencies and dispositions for career self-management and career success, but to date there has been minimal empirical work investigating the predictive value of these competencies and dispositions to career success in the boundaryless or protean career. This program of research employed competencies and dispositions from boundaryless and protean career theory to predict career success in professional artists and tertiary arts graduates. These competencies and dispositions were placed into context using individual and contextual career development influences suggested by the Systems Theory Framework of career development (McMahon & Patton, 1995; Patton & McMahon, 1999, 2006a). Four substantive studies were conducted, using online surveys with professional artists and tertiary arts students / graduates, which were preceded by a pilot study for measure development. A largely quantitative approach to the program of research was preferred, in the interests of generalisability of findings. However, at the time of data collection, there were no quantitative measures available which addressed the constructs of interest. Brief scales of Career Management Competence based on the Australian Blueprint for Career Development (Haines, Scott, & Lincoln, 2003), Protean Career Success Orientation based on the underlying dispositions for career success suggested by protean career theory, and Career Development Influences based on the Systems Theory Framework of career development (McMahon & Patton, 1995; Patton & McMahon, 1999, 2006a) were constructed and validated via a process of pilot testing and exploratory factor analyses. This process was followed by confirmatory factor analyses with data collected from two samples: 310 professional artists, and 218 graduating arts students who participated at time 1 (i.e., at the point of undergraduate course completion in October, 2005). Confirmatory factor analyses via Structural Equation Modelling conducted in Study 1 revealed that the scales would benefit from some respecification, and so modifications were made to the measures to enhance their validity and reliability. The three scales modified and validated in Study 1 were then used in Studies 3 and 4 as potential predictors of career success for the two groups of artists under investigation, along with relevant sociodemographic variables. The aim of the Study 2 was to explore the construct of career success in the two groups of artists studied. Each participant responded to an open-ended question asking them to define career success. The responses for professional artists were content analysed using emergent coding with two coders. The codebook was later applied to the arts students' definitions. The majority of the themes could be grouped into four main categories: internal definitions; financial recognition definitions; contribution definitions; and non-financial recognition definitions. Only one third of the definition themes in the professional artists' and arts graduates' definitions of career success were categorised as relating to financial recognition. Responses within the financial recognition category also indicated that many of the artists aspired only to a regular subsistence level of arts income (although a small number of the arts graduates did aspire to fame and fortune). The second section of the study investigated the statistical relationships between the five different measures of career success for each career success definitional category and overall. The professional artists' and arts graduates' surveys contained several measures of career success, including total earnings over the previous 12 months, arts earnings over the previous 12 months, 1-6 self-rated total employability, 1-6 self-rated arts employability, and 1-6 self-rated self-defined career success. All of the measures were found to be statistically related to one another, but a very strong statistical relationship was identified between each employability measure and its corresponding earnings measure for both of the samples. Consequently, it was decided to include only the earnings measures (earnings from arts, and earnings overall) and the self-defined career success rating measure in the later studies. Study 3 used the career development constructs validated in Study 1, sociodemographic variables, and the career success measures explored in Study 2 via Classification and Regression Tree (CART - Breiman, Friedman, Olshen, & Stone, 1984) style decision trees with v-fold crossvalidation pruning using the 1 SE rule. CART decision trees are a nonparametric analysis technique which can be used as an alternative to OLS or hierarchical regression in the case of data which violates parametric statistical assumptions. The three optimal decision trees for total earnings, arts earnings and self defined career success ratings explained a large proportion of the variance in their respective target variables (R2 between 0.49 and 0.68). The Career building subscale of the Career Management Competence scale, pertaining to the ability to manage the external aspects of a career, was the most consistent predictor of all three career success measures (and was the strongest predictor for two of the three trees), indicating the importance of the artists' abilities to secure work and build the external aspects of a career. Other important predictors included the Self management subscale of the Career Management Competence scale, Protean Career Success Orientation, length of time working in the arts, and the positive role of interpersonal influences, skills and abilities, and interests and beliefs from the Career Development Influences scale. Slightly different patterns of predictors were found for the three different career success measures. Study 4 also involved the career development constructs validated in Study 1, sociodemographic variables, and the career success measures explored in Study 2 via CART style decision trees. This study used a prospective repeated measures design where the data for the attribute variables were gathered at the point of undergraduate course completion, and the target variables were measured one year later. Data from a total of 122 arts students were used, as 122 of the 218 students who responded to the survey at time 1 (October 2005) also responded at time 2 (October 2006). The resulting optimal decision trees had R2 values of between 0.33 and 0.46. The values were lower than those for the professional artists' decision trees, and the trees themselves were smaller, but the R2 values nonetheless indicated that the arts students' trees possessed satisfactory explanatory power. The arts graduates' Career building scores at time 1 were strongly predictive of all three career success measures at time 2, a similar finding to the professional artists' trees. A further similarity between the trees for the two samples was the strong statistical relationship between Career building, Self management, and Protean Career Success Orientation. However, the most important variable in the total earnings tree was arts discipline category. Technical / design arts graduates consistently earned more overall than arts graduates from other disciplines. Other key predictors in the arts graduates' trees were work experience in arts prior to course completion, positive interpersonal influences, and the positive influence of skills and abilities and interests and beliefs on career development. The research program findings represent significant contributions to existing knowledge about artists' career development and success, and also the transition from higher education to the world of work, with specific reference to arts and creative industries programs. It also has implications for theory relating to career success and protean / boundaryless careers.

The inquiry documented in this thesis is located at the nexus of technological innovation and traditional schooling. As we enter the second decade of a new century, few would argue against the increasingly urgent need to integrate digital literacies with traditional academic knowledge. Yet, despite substantial investments from governments and businesses, the adoption and diffusion of contemporary digital tools in formal schooling remain sluggish. To date, research on technology adoption in schools tends to take a deficit perspective of schools and teachers, with the lack of resources and teacher ‘technophobia’ most commonly cited as barriers to digital uptake. Corresponding interventions that focus on increasing funding and upskilling teachers, however, have made little difference to adoption trends in the last decade. Empirical evidence that explicates the cultural and pedagogical complexities of innovation diffusion within long-established conventions of mainstream schooling, particularly from the standpoint of students, is wanting. To address this knowledge gap, this thesis inquires into how students evaluate and account for the constraints and affordances of contemporary digital tools when they engage with them as part of their conventional schooling. It documents the attempted integration of a student-led Web 2.0 learning initiative, known as the Student Media Centre (SMC), into the schooling practices of a long-established, high-performing independent senior boys’ school in urban Australia. The study employed an ‘explanatory’ two-phase research design (Creswell, 2003) that combined complementary quantitative and qualitative methods to achieve both breadth of measurement and richness of characterisation. In the initial quantitative phase, a self-reported questionnaire was administered to the senior school student population to determine adoption trends and predictors of SMC usage (N=481). Measurement constructs included individual learning dispositions (learning and performance goals, cognitive playfulness and personal innovativeness), as well as social and technological variables (peer support, perceived usefulness and ease of use). Incremental predictive models of SMC usage were conducted using Classification and Regression Tree (CART) modelling: (i) individual-level predictors, (ii) individual and social predictors, and (iii) individual, social and technological predictors. Peer support emerged as the best predictor of SMC usage. Other salient predictors include perceived ease of use and usefulness, cognitive playfulness and learning goals. On the whole, an overwhelming proportion of students reported low usage levels, low perceived usefulness and a lack of peer support for engaging with the digital learning initiative. The small minority of frequent users reported having high levels of peer support and robust learning goal orientations, rather than being predominantly driven by performance goals. These findings indicate that tensions around social validation, digital learning and academic performance pressures influence students’ engagement with the Web 2.0 learning initiative. The qualitative phase that followed provided insights into these tensions by shifting the analytics from individual attitudes and behaviours to shared social and cultural reasoning practices that explain students’ engagement with the innovation. Six indepth focus groups, comprising 60 students with different levels of SMC usage, were conducted, audio-recorded and transcribed. Textual data were analysed using Membership Categorisation Analysis. Students’ accounts converged around a key proposition. The Web 2.0 learning initiative was useful-in-principle but useless-in-practice. While students endorsed the usefulness of the SMC for enhancing multimodal engagement, extending peer-topeer networks and acquiring real-world skills, they also called attention to a number of constraints that obfuscated the realisation of these design affordances in practice. These constraints were cast in terms of three binary formulations of social and cultural imperatives at play within the school: (i) ‘cool/uncool’, (ii) ‘dominant staff/compliant student’, and (iii) ‘digital learning/academic performance’. The first formulation foregrounds the social stigma of the SMC among peers and its resultant lack of positive network benefits. The second relates to students’ perception of the school culture as authoritarian and punitive with adverse effects on the very student agency required to drive the innovation. The third points to academic performance pressures in a crowded curriculum with tight timelines. Taken together, findings from both phases of the study provide the following key insights. First, students endorsed the learning affordances of contemporary digital tools such as the SMC for enhancing their current schooling practices. For the majority of students, however, these learning affordances were overshadowed by the performative demands of schooling, both social and academic. The student participants saw engagement with the SMC in-school as distinct from, even oppositional to, the conventional social and academic performance indicators of schooling, namely (i) being ‘cool’ (or at least ‘not uncool’), (ii) sufficiently ‘compliant’, and (iii) achieving good academic grades. Their reasoned response therefore, was simply to resist engagement with the digital learning innovation. Second, a small minority of students seemed dispositionally inclined to negotiate the learning affordances and performance constraints of digital learning and traditional schooling more effectively than others. These students were able to engage more frequently and meaningfully with the SMC in school. Their ability to adapt and traverse seemingly incommensurate social and institutional identities and norms is theorised as cultural agility – a dispositional construct that comprises personal innovativeness, cognitive playfulness and learning goals orientation. The logic then is ‘both and’ rather than ‘either or’ for these individuals with a capacity to accommodate both learning and performance in school, whether in terms of digital engagement and academic excellence, or successful brokerage across multiple social identities and institutional affiliations within the school. In sum, this study takes us beyond the familiar terrain of deficit discourses that tend to blame institutional conservatism, lack of resourcing and teacher resistance for low uptake of digital technologies in schools. It does so by providing an empirical base for the development of a ‘third way’ of theorising technological and pedagogical innovation in schools, one which is more informed by students as critical stakeholders and thus more relevant to the lived culture within the school, and its complex relationship to students’ lives outside of school. It is in this relationship that we find an explanation for how these individuals can, at the one time, be digital kids and analogue students.

Saltmarshes provide a diverse range of ecosystem services and are protected under a number of international designations. Nevertheless they are generally declining in extent in the United Kingdom and North West Europe. The drivers of this decline are complex and poorly understood. When considering mitigation and management for future ecosystem service provision it will be important to understand why, where, and to what extent decline is likely to occur. Few studies have attempted to forecast saltmarsh morphodynamics at a system level over decadal time scales. There is no synthesis of existing knowledge available for specific site predictions nor is there a formalised framework for individual site assessment and management. This project evaluates the extent to which machine learning model approaches (boosted regression trees, neural networks and Bayesian networks) can facilitate synthesis of information and prediction of decadal-scale morphological tendencies of saltmarshes. Importantly, data-driven predictions are independent of the assumptions underlying physically-based models, and therefore offer an additional opportunity to crossvalidate between two paradigms. Marsh margins and interiors are both considered but are treated separately since they are regarded as being sensitive to different process suites. The study therefore identifies factors likely to control morphological trajectories and develops geospatial methodologies to derive proxy measures relating to controls or processes. These metrics are developed at a high spatial density in the order of tens of metres allowing for the resolution of fine-scale behavioural differences. Conventional statistical approaches, as have been previously adopted, are applied to the dataset to assess consistency with previous findings, with some agreement being found. The data are subsequently used to train and compare three types of machine learning model. Boosted regression trees outperform the other two methods in this context. The resulting models are able to explain more than 95% of the variance in marginal changes and 91% for internal dynamics. Models are selected based on validation performance and are then queried with realistic future scenarios which represent altered input conditions that may arise as a consequence of future environmental change. Responses to these scenarios are evaluated, suggesting system sensitivity to all scenarios tested and offering a high degree of spatial detail in responses. While mechanistic interpretation of some responses is challenging, process-based justifications are offered for many of the observed behaviours, providing confidence that the results are realistic. The work demonstrates a potentially powerful alternative (and complement) to current morphodynamic models that can be applied over large areas with relative ease, compared to numerical implementations. Powerful analyses with broad scope are now available to the field of coastal geomorphology through the combination of spatial data streams and machine learning. Such methods are shown to be of great potential value in support of applied management and monitoring interventions.

[pt] Este trabalho propõe um novo modelo linear por partes para a extração de regras de conhecimento de banco de dados. O modelo é uma heurística baseada em análise de árvore de regressão, como introduzido por Friedman (1979) e discutido em detalhe por Breiman (1984). A motivação desta pesquisa é trazer uma nova abordagem combinando técnicas estatísticas de modelagem e um algoritmo de busca por quebras eficiente. A decisão de quebra usada no algoritmo de busca leva em consideração informações do ajuste de equações lineares e foi implementado tendo por inspiração o trabalho de Tsay (1989). Neste, ele sugere um procedimento para construção um modelo para a análise de séries temporais chamado TAR (threshold autoregressive model), introduzido por Tong (1978) e discutido em detalhes por Tong e Lim (1980) e Tong (1983). O modelo TAR é um modelo linear por partes cuja idéia central é alterar os parâmetros do modelo linear autoregressivo de acordo com o valor de uma variável observada, chamada de variável limiar. No trabalho de Tsay, a Identificação do número e localização do potencial limiar era baseada na analise de gráficos. A idéia foi então criar um novo algoritmo todo automatizado. Este processo é um algoritmo que preserva o método de regressão por mínimos quadrados recursivo (MQR) usado no trabalho de Tsay. Esta talvez seja uma das grandes vantagens da metodologia introduzida neste trabalho, visto que Cooper (1998) em seu trabalho de análise de múltiplos regimes afirma não ser possível testar cada quebra. Da combinação da árvore de decisão com a técnica de regressão (MQR), o modelo se tornou o TS-TARX (Tree Structured - Threshold AutoRegression with eXternal variables). O procedimento consiste numa busca em árvore binária calculando a estatística F para a seleção das variáveis e o critério de informação BIC para a seleção dos modelos. Ao final, o algoritmo gera como resposta uma árvore de decisão (por meio de regras) e as equações de regressão estimadas para cada regime da partição. A principal característica deste tipo de resposta é sua fácil interpretação. O trabalho conclui com algumas aplicações em bases de dados padrões encontradas na literatura e outras que auxiliarão o entendimento do processo implementado. / [en] This research work proposes a new piecewise linear model to extract knowledge rules from databases. The model is an heuristic based on analysis of regression trees, introduced by Friedman (1979) and discussed in detail by Breiman (1984). The motivation of this research is to come up with a new approach combining both statistical modeling techniques and an efficient split search algorithm. The split decision used in the split search algorithm counts on information from adjusted linear equation and was implemented inspired by the work of Tsay (1989). In his work, he suggests a model-building procedure for a nonlinear time series model called by TAR (threshold autoregressive model), first proposed by Tong (1978) and discussed in detail by Tong and Lim (1980) and Tong (1983). The TAR model is a piecewise linear model which main idea is to set the coefficients of a linear autoregressive process in accordance with a value of observed variable, called by threshold variable. Tsay`s identification of the number and location of the potential thresholds was based on supplementary graphic devices. The idea is to get the whole process automatic on a new model-building process. This process is an algorithm that preserves the method of regression by recursive least squares (RLS) used in Tsay`s work. This regression method allowed the test of all possibilities of data split. Perhaps that is the main advantage of the methodology introduced in this work, seeing that Cooper, S. (1998) said about the impossibility of testing each break.Thus, combining decision tree methodology with a regression technique (RLS), the model became the TS-TARX (Tree Structured - Threshold AutoRegression with eXternal variables). It searches on a binary tree calculating F statistics for variable selection and the information criteria BIC for model selection. In the end, the algorithm produces as result a decision tree and a regression equation adjusted to each regime of the partition defined by the decision tree. Its major advantage is easy interpretation.This research work concludes with some applications in benchmark databases from literature and others that helps the understanding of the algorithm process.

No description available.

Traffic congestion is a severe problem in urban areas and it leads to the emission of greenhouse gases and air pollution. In general, drivers lack knowledge of the location and availability of free parking spaces in urban cities. This leads to people driving around searching for parking places, and about one-third of traffic congestion in cities is due to drivers searching for an available parking lot. In recent years, various solutions to provide parking information ahead have been proposed. The vast majority of these solutions have been applied in large cities, such as Beijing and San Francisco. This thesis has been conducted in collaboration with Knowit and Dukaten to predict parking occupancy in car parks one hour ahead in the relatively small city of Linköping. To make the predictions, this study has investigated the possibility to use long short-term memory and gradient boosting regression trees, trained on historical parking data. To enhance decision making, the predictive uncertainty was estimated using the novel approach Monte Carlo dropout for the former, and quantile regression for the latter. This study reveals that both of the models can predict parking occupancy ahead of time and they are found to excel in different contexts. The inclusion of exogenous features can improve prediction quality. More specifically, we found that incorporating hour of the day improved the models’ performances, while weather features did not contribute much. As for uncertainty, the employed method Monte Carlo dropout was shown to be sensitive to parameter tuning to obtain good uncertainty estimates.

Indiana University-Purdue University Indianapolis (IUPUI) / Magnetic resonance (MR) spectroscopy is a neuroimaging technique. It is widely used to quantify the concentration of important metabolites in a brain tissue. Imbalance in concentration of brain metabolites has been found to be associated with development of neurological impairment. There has been increasing trend of using MR spectroscopy as a diagnosis tool for neurological disorders. We established statistical methodology to analyze data obtained from the MR spectroscopy in the context of the HIV associated neurological disorder. First, we have developed novel methodology to study the association of marker of neurological disorder with MR spectrum from brain and how this association evolves with time. The entire problem fits into the framework of scalar-on-function regression model with individual spectrum being the functional predictor. We have extended one of the existing cross-sectional scalar-on-function regression techniques to longitudinal set-up. Advantage of proposed method includes: 1) ability to model flexible time-varying association between response and functional predictor and (2) ability to incorporate prior information. Second part of research attempts to study the influence of the clinical and demographic factors on the progression of brain metabolites over time. In order to understand the influence of these factors in fully non-parametric way, we proposed LongCART algorithm to construct regression tree with longitudinal data. Such a regression tree helps to identify smaller subpopulations (characterized by baseline factors) with differential longitudinal profile and hence helps us to identify influence of baseline factors. Advantage of LongCART algorithm includes: (1) it maintains of type-I error in determining best split, (2) substantially reduces computation time and (2) applicable even observations are taken at subject-specific time-points. Finally, we carried out an in-depth analysis of longitudinal changes in the brain metabolite concentrations in three brain regions, namely, white matter, gray matter and basal ganglia in chronically infected HIV patients enrolled in HIV Neuroimaging Consortium study. We studied the influence of important baseline factors (clinical and demographic) on these longitudinal profiles of brain metabolites using LongCART algorithm in order to identify subgroup of patients at higher risk of neurological impairment. / Partial research support was provided by the National Institutes of Health grants U01-MH083545, R01-CA126205 and U01-CA086368